from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2022-12-28 14:02:35.095431
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'1. Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64('2020-12-06'),
'red', 'inside top left'),
'2. Soft Lockdown': (np.datetime64('2020-12-06'), np.datetime64('2020-12-27'),
'orange', 'inside top left'),
'Weihnachten 2020': (np.datetime64('2020-12-24'), np.datetime64('2020-12-27'),
'blue', 'inside top left'),
'3. Lockdown': (np.datetime64('2020-12-27'), np.datetime64(TODAY),
'red', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Wed, 28, Dec, 2022
Time: 14:02:42
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -51.3148
Nobs: 884.000 HQIC: -51.6157
Log likelihood: 11697.4 FPE: 3.18214e-23
AIC: -51.8019 Det(Omega_mle): 2.87575e-23
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.297323 0.049403 6.018 0.000
L1.Burgenland 0.105843 0.033888 3.123 0.002
L1.Kärnten -0.106642 0.018205 -5.858 0.000
L1.Niederösterreich 0.212969 0.071071 2.997 0.003
L1.Oberösterreich 0.083572 0.067234 1.243 0.214
L1.Salzburg 0.250608 0.035985 6.964 0.000
L1.Steiermark 0.030011 0.047258 0.635 0.525
L1.Tirol 0.126848 0.038441 3.300 0.001
L1.Vorarlberg -0.061710 0.033081 -1.865 0.062
L1.Wien 0.065407 0.059968 1.091 0.275
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.062523 0.101460 0.616 0.538
L1.Burgenland -0.009181 0.069595 -0.132 0.895
L1.Kärnten 0.049273 0.037388 1.318 0.188
L1.Niederösterreich -0.171576 0.145959 -1.176 0.240
L1.Oberösterreich 0.359815 0.138080 2.606 0.009
L1.Salzburg 0.285836 0.073902 3.868 0.000
L1.Steiermark 0.109253 0.097055 1.126 0.260
L1.Tirol 0.319213 0.078946 4.043 0.000
L1.Vorarlberg 0.025206 0.067938 0.371 0.711
L1.Wien -0.024514 0.123156 -0.199 0.842
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.200868 0.025691 7.818 0.000
L1.Burgenland 0.090429 0.017623 5.131 0.000
L1.Kärnten -0.008895 0.009467 -0.940 0.347
L1.Niederösterreich 0.267491 0.036959 7.237 0.000
L1.Oberösterreich 0.110761 0.034964 3.168 0.002
L1.Salzburg 0.053611 0.018713 2.865 0.004
L1.Steiermark 0.015264 0.024576 0.621 0.535
L1.Tirol 0.101899 0.019990 5.097 0.000
L1.Vorarlberg 0.057000 0.017203 3.313 0.001
L1.Wien 0.112540 0.031185 3.609 0.000
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.104900 0.026316 3.986 0.000
L1.Burgenland 0.047888 0.018051 2.653 0.008
L1.Kärnten -0.016794 0.009697 -1.732 0.083
L1.Niederösterreich 0.197828 0.037858 5.226 0.000
L1.Oberösterreich 0.276872 0.035814 7.731 0.000
L1.Salzburg 0.118024 0.019168 6.157 0.000
L1.Steiermark 0.099941 0.025174 3.970 0.000
L1.Tirol 0.126440 0.020477 6.175 0.000
L1.Vorarlberg 0.070088 0.017621 3.977 0.000
L1.Wien -0.026049 0.031944 -0.815 0.415
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.132591 0.047437 2.795 0.005
L1.Burgenland -0.053914 0.032539 -1.657 0.098
L1.Kärnten -0.036766 0.017480 -2.103 0.035
L1.Niederösterreich 0.166339 0.068242 2.437 0.015
L1.Oberösterreich 0.132440 0.064559 2.051 0.040
L1.Salzburg 0.290763 0.034552 8.415 0.000
L1.Steiermark 0.033760 0.045377 0.744 0.457
L1.Tirol 0.161059 0.036911 4.363 0.000
L1.Vorarlberg 0.108096 0.031764 3.403 0.001
L1.Wien 0.067183 0.057581 1.167 0.243
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.062515 0.037684 1.659 0.097
L1.Burgenland 0.038624 0.025849 1.494 0.135
L1.Kärnten 0.050328 0.013886 3.624 0.000
L1.Niederösterreich 0.226838 0.054212 4.184 0.000
L1.Oberösterreich 0.267532 0.051285 5.217 0.000
L1.Salzburg 0.060127 0.027448 2.191 0.028
L1.Steiermark -0.007714 0.036048 -0.214 0.831
L1.Tirol 0.156293 0.029322 5.330 0.000
L1.Vorarlberg 0.069285 0.025233 2.746 0.006
L1.Wien 0.076838 0.045742 1.680 0.093
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.189466 0.045280 4.184 0.000
L1.Burgenland 0.017340 0.031059 0.558 0.577
L1.Kärnten -0.059351 0.016686 -3.557 0.000
L1.Niederösterreich -0.096613 0.065139 -1.483 0.138
L1.Oberösterreich 0.176247 0.061623 2.860 0.004
L1.Salzburg 0.061693 0.032981 1.871 0.061
L1.Steiermark 0.227711 0.043314 5.257 0.000
L1.Tirol 0.485425 0.035233 13.778 0.000
L1.Vorarlberg 0.051170 0.030320 1.688 0.091
L1.Wien -0.051317 0.054963 -0.934 0.350
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.156829 0.051166 3.065 0.002
L1.Burgenland -0.000189 0.035097 -0.005 0.996
L1.Kärnten 0.066504 0.018855 3.527 0.000
L1.Niederösterreich 0.201774 0.073607 2.741 0.006
L1.Oberösterreich -0.070978 0.069633 -1.019 0.308
L1.Salzburg 0.221389 0.037268 5.940 0.000
L1.Steiermark 0.111846 0.048944 2.285 0.022
L1.Tirol 0.085336 0.039812 2.143 0.032
L1.Vorarlberg 0.124018 0.034261 3.620 0.000
L1.Wien 0.104954 0.062107 1.690 0.091
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.358700 0.030326 11.828 0.000
L1.Burgenland 0.007735 0.020802 0.372 0.710
L1.Kärnten -0.025668 0.011175 -2.297 0.022
L1.Niederösterreich 0.229916 0.043627 5.270 0.000
L1.Oberösterreich 0.151102 0.041272 3.661 0.000
L1.Salzburg 0.052643 0.022089 2.383 0.017
L1.Steiermark -0.016039 0.029009 -0.553 0.580
L1.Tirol 0.122766 0.023597 5.203 0.000
L1.Vorarlberg 0.071661 0.020307 3.529 0.000
L1.Wien 0.048732 0.036811 1.324 0.186
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.039110 0.163926 0.183288 0.170384 0.145819 0.130537 0.067541 0.220402
Kärnten 0.039110 1.000000 0.002705 0.132833 0.027524 0.099874 0.430986 -0.048787 0.101798
Niederösterreich 0.163926 0.002705 1.000000 0.349841 0.173244 0.318539 0.134619 0.194477 0.341724
Oberösterreich 0.183288 0.132833 0.349841 1.000000 0.235930 0.345015 0.182607 0.181336 0.273908
Salzburg 0.170384 0.027524 0.173244 0.235930 1.000000 0.156246 0.140205 0.154345 0.141412
Steiermark 0.145819 0.099874 0.318539 0.345015 0.156246 1.000000 0.165694 0.150640 0.097391
Tirol 0.130537 0.430986 0.134619 0.182607 0.140205 0.165694 1.000000 0.124807 0.164324
Vorarlberg 0.067541 -0.048787 0.194477 0.181336 0.154345 0.150640 0.124807 1.000000 0.020757
Wien 0.220402 0.101798 0.341724 0.273908 0.141412 0.097391 0.164324 0.020757 1.000000